Tobacco and alcohol consumption in relation to oral cancer.

The present case control study was done on 50 cases of oral cancer and 50 controls These two groups are compared after taking the history in detail with special emphasis on the tobacco and alcohol intake. My various statistical tests, we correlated the strength of association between tobacco/alcohol intake and development of oral cancer. Tobacco only and combined exposure to tobacco and alcohol was found to be strongly related to the development of oral cancer but alcohol alone does not have significant role in causation of oral cancer.

Head and neck masses in children: A clinicopathological study.

Fifty children with head and neck masses were evaluated clinically and pathologically. Inflammatory swellings constituted the majority (54%). Congenital - developmental malformations were the next common lesions (30%) and neoplastic masses constituted the rest (16%). Tuberculous lymphadenitis was found to be the single most common etiology (28%). Among congenital-developmental malformations, cystic hygroma was the most frequent lesion. The incidence of branchial cleft abnormalities was found to be low and no thyroglossal duct cyst was observed in this series. Among the neoplastic masses malignant lesions were more common than benign tumours and lymphoma was the most common head and neck malignancy observed.

An evidential extension of the MRII training algorithm for detecting erroneous MADALINE responses.

This paper integrates the evidential reasoning methodology with the parallel distributed learning paradigm of artificial neural networks (ANN). As such, this work presents an algorithm for the detection and, if possible, subsequent correction of the errors in the neuron responses in the output layer of the multiple adaptive linear element (MADALINE) ANN. A geometrical perspective of the MADALINE ANN processing methodology is provided. This perspective is then used to formulate a statistical specification to identify and quantify the sources of uncertainties in the MADALINE processing methodology. A new algorithm, EMRII, is then developed as an extension to the original MRII (MADELINE rule II) algorithm, to formulate support and plausibility measures based on the statistical specification. The support and plausibility measures, thus formulated, are indicative of the degree of confidence of the ANN, in regards to the correctness of its outputs. Based on the support measure, a scheme utilizing two thresholds is proposed to facilitate the interpretation of the support values for error prediction in the ANN responses. Finally, simulation results for the application of the EMRII algorithm in the prediction of erroneous responses in an example problem is presented. These simulation results highlight the error detection capabilities of the EMRII algorithm.